对话式AI增强的探索系统用于查询自然历史博物馆大规模数字化收藏
Conversational AI-Enhanced Exploration System to Query Large-Scale Digitised Collections of Natural History Museums
AI总结：
本文提出利用对话式AI查询自然历史博物馆大规模数字化收藏的系统，通过交互式地图和自然语言代理实现高效的数据检索与探索。
摘要：
近年来，自然历史博物馆的数字化努力产生了大量收藏数据，但其规模和科学复杂性往往阻碍了公众的访问和理解。传统数据管理工具，如数据库，通过关键词搜索限制探索，或需要专门的模式知识。本文提出了一种系统设计，利用对话式AI来查询澳大利亚博物馆生命科学收藏中近170万条数字化标本记录。该系统通过人本设计过程进行设计和开发，包含一个交互式地图用于视觉空间探索和一个自然语言对话代理，用于检索详细的标本数据并回答收藏特定的问题。该系统利用现代大型语言模型的功能调用能力，动态从外部API检索结构化数据，从而实现快速、实时地与大量但频繁更新的数据集进行交互。我们的工作提供了一种将大型博物馆收藏与基于自然语言的查询连接的新方法，并为未来自然历史博物馆的科学AI代理设计提供了启示。
英文摘要：
Recent digitisation efforts in natural history museums have produced large volumes of collection data, yet their scale and scientific complexity often hinder public access and understanding. Conventional data management tools, such as databases, restrict exploration through keyword-based search or require specialised schema knowledge. This paper presents a system design that uses conversational AI to query nearly 1.7 million digitised specimen records from the life-science collections of the Australian Museum. Designed and developed through a human-centred design process, the system contains an interactive map for visual-spatial exploration and a natural-language conversational agent that retrieves detailed specimen data and answers collection-specific questions. The system leverages function-calling capabilities of contemporary large language models to dynamically retrieve structured data from external APIs, enabling fast, real-time interaction with extensive yet frequently updated datasets. Our work provides a new approach of connecting large museum collections with natural language-based queries and informs future designs of scientific AI agents for natural history museums.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
